The matrix effect is the effect on an analytical assay caused by all other sample components except the specific compound (analyte) to be analyzed.
Matrix effects are observed either as a loss in response, resulting in an underestimation of the amount of analyte or an increase in response, producing an overestimated result. These effects have long been associated with bioanalytical techniques. However, their evaluation for each assay and each sample matrix can be very time-consuming. Also, these extra evaluation steps bring additional costs.
So, how important is evaluating the matrix effects when analyzing bioprocess samples? What are the best approaches to evaluate the effect? And in what situations is the matrix effect acceptable?
The Importance of Matrix Effect Evaluation
To release final product for use in patients, the product undergoes different quantitative or quantitative analytical procedures. Based on the obtained data, a decision is made to approve or reject the product batch. Therefore, showing that generated analytical data is accurate and no matrix effect is involved is necessary. During the assay development and validation, the impact of the sample matrix is investigated. Each assay is developed to allow monitoring of the matrix effect and, if possible, eliminate it.
Methods to Evaluate and Eliminate
A few quick, qualitative options exist to determine whether a matrix effect is present, such as the dilution-based method. However, this article will focus on a few quantitative methods often used for bioanalytical techniques.
This method allows for quantifying the matrix effect for one specific concentration.
At this concentration, the analyte is measured in the matrix and subsequently measured in a solvent known not to induce any effect. The analyte signal in the matrix is then divided by the analyte signal in the solvent and multiplied by a hundred, resulting in the percentage of matrix effect. If the percentage is below a hundred, the matrix effect results in a suppression of the result. When it is above a hundred, it causes enhancement of the result.
This method is useful when only this concentration is relevant. It doesn’t necessarily provide any indication of other analyte concentrations.
In this method, the matrix effect is measured with the signal-based method but for a range of analyte concentrations. The concentration-based method is used to show that the matrix effect is not analyte concentration dependent.
This method is particularly relevant when a blank matrix is not available. Different analyte concentrations are measured in solvent and the matrix, and obtained data are plotted in a graph, and linear regression model is used to generate a slope value.
The slope of calibration analyzed in the matrix is then divided by the slope of the calibration curve prepared in the solvent. The ratio is then multiplied by a hundred to generate %ME. For %ME >100% matrix results in overestimation, and for %ME < 100% tested matrix leads to signal suppression.
When a matrix effect is present, the effect-causing component must be removed from the sample before analysis. Unfortunately, this is not always an option.
In those cases, matrix minimization (dilution) will provide a way forward. Matrix minimization is especially useful when the analytical technique has sensitivity to spare. Eliminating matrix effect for the particular matrix is proved during the assay development or validation.
When Can You Ignore Matrix Effect?
Theoretically, samples consisting a pure compound could be ignored for matrix testing. However, even a supposedly pure compound may contain other elements in some cases. Elements such as reaction impurities or by-products may lead to matrix effects. Especially during process development, it is challenging to remove the matrix effect. This is due to the large number of matrices generated at each step of upstream or downstream process development.
Analysis of PD samples usually serves to monitor processes and provide process developers with an indication of whether changes in certain process parameters have beneficial or undesirable consequences. As the absolute value of the analysis is less important during the process development compared to batch release analysis, matrix effects are often not completely removed. Instead, they are only monitored using a spike recovery approach. This approach enables sample analysis with sufficient information on a potential matrix effect while saving time and resources.